Andreas Merentitis

TL;DR: This paper highlights the two awarded research contributions, which investigated different approaches for the fusion of hyperspectral and LiDAR data, including a combined unsupervised and supervised classification scheme, and a graph-based method for the Fusion of spectral, spatial, and elevation information.

TL;DR: Monitoring the activities of daily living (ADLs) and detection of deviations from previous patterns is crucial to assessing the ability of an elderly person to live independently in their community and in early detection of upcoming critical situations.

TL;DR: This work introduces a consistent unified framework that jointly considers all steps in the hyperspectral image classification chain from a bias-variance decomposition perspective and shows how state-of-the-art techniques in feature extraction, ensemble-based classification, and post-classification segmentation are related to the bias-Variance tradeoff and how this relation can be used to improve classification accuracy.

TL;DR: The status of semantic support for the IoT field is presented by briefly surveying research efforts in IoT dealing with semantic concerns at the architecture level and an ontology used to semantically describe the adaptation options of the dynamic protocol stacks for future communication devices is presented.

TL;DR: A hierarchical hidden Markov model for sound event detection that automatically clusters the inherent structure of the events into sub-events is presented and can be used as a meta-classifier, although in the particular application this did not lead to an increase in performance on the test dataset.